SEA-NLI: Natural Language Inference as a Lens into Southeast Asian Cultural Understanding

Peerawat Chomphooyod; Jian Gang Ngui; Yosephine Susanto; Attapol T. Rutherford; Alham Fikri Aji; Sarana Nutanong; Can Udomcharoenchaikit; Peerat Limkonchotiwat

arXiv · 2606.03284

SEA-NLI: Natural Language Inference as a Lens into Southeast Asian Cultural Understanding

Peerawat Chomphooyod·Jian Gang Ngui·Yosephine Susanto·Attapol T. Rutherford·Alham Fikri Aji·Sarana Nutanong·Can Udomcharoenchaikit·Peerat Limkonchotiwat

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Abstract

Frontier LLMs perform well in Western contexts, but remain poorly tested on underrepresented cultures such as those in Southeast Asia (SEA). Existing NLI benchmarks are largely Western-centric, translation-derived, or monolingual, limiting their ability to measure culturally grounded reasoning. We introduce SEA-NLI, a native, culturally grounded NLI benchmark covering eight SEA countries in English and native regional languages, verified by native speakers. Across 17 encoder and decoder models, we observe a low performance from all models, especially for knowledge-intensive categories such as Languages and Science and Technology. Our analysis shows that failure cases mainly stem from missing SEA cultural knowledge: SEA-adapted models and culture-aware prompting improve performance, while CoT prompting offers limited gains.

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Peerawat Chomphooyod, Jian Gang Ngui, Yosephine Susanto, Attapol T. Rutherford, Alham Fikri Aji, Sarana Nutanong, Can Udomcharoenchaikit, Peerat Limkonchotiwat. 2026-06-02. SEA-NLI: Natural Language Inference as a Lens into Southeast Asian Cultural Understanding. https://arxiv.org/abs/2606.03284

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